Exploring image registration techniques for layered sensing
Why this work is in the frame
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Bibliographic record
Abstract
In this paper we evaluate several methods to register and stabilize a motion imagery video sequence under the layered sensing construction. Layered sensing is a new construct in the repertoire of the US Air Force. Under the layered sensing paradigm, an area is surveyed by a multitude of sensors at many different altitudes and operating across many modalities. This combination of sensors provides better insight into a situation than could ever be achieved with a single sensor. A fundamental requirement to utilize this technology is to first register and stabilize the data from each of the individual sensors. The contribution of this paper is to explore and provide a preliminary evaluation of techniques for image registration of Electro-Optical (EO) video sequences taken from Wide Area Persistent Surveillance (WAPS) platforms whose views are centered on a city. Additionally, evaluation metrics for such techniques are described and explored.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it